Spectral-domain optical coherence tomography (“SD-OCT”) was recently established as a real-time technique for investigating the depth structure of biomedical tissue with the purpose of non-invasive optical diagnostics. A detailed description of SD-OCT techniques is described in Fercher et al. “Measurement of Intraocular Distances by Backscattering Spectral Interferometry”, Optics Communications, 117(1-2), 43 (1995) and Wojtkowski et al. “In vivo human retinal imaging by Fourier domain optical coherence tomography”, J. Biomed. Opt. 7(3), 457 (2002). Compared to the commercially available time-domain OCT systems, SD-OCT techniques provide for video-rate OCT scans, are relative fast, as shown in Nassif et al. “In vivo human retinal imaging by ultrahigh-speed spectral domain optical coherence tomography”, Opt. Lett. 29(5), 480 (2004), and provide a good sensitivity, as described in Leitgeb et al. “Performance of Fourier domain vs. time domain optical coherence tomography”, Opt. Express, 11(8), 889 (2003) and de Boer et al. “Improved signal-to-noise ratio in spectral-domain compared with time-domain optical coherence tomography”, Opt. Lett., 28(21), 2067 (2003). An exemplary arrangement which can be used for video-rate OCT scans has been described in International application number PCT/US03/02349 filed Jan. 24, 2003 and in Nassif et al. “In vivo human retinal imaging by ultrahigh-speed spectral domain optical coherence tomography”, Opt. Lett., 29(5), 480 (2004).
Similar technology, e.g., Optical Frequency Domain Imaging (“OFDI”), can use a rapidly tuned laser to measure the wavelength resolved interference as described in Chinn et al. “Optical coherence tomography using a frequency tunable optical source”, Opt. Lett. 22(5), 340 (1997), and Yun et al. “High-speed optical frequency-domain imaging”, Opt. Express 11(22), 2953 (2003) and International Application PCT/US04/029148 filed Sep. 8, 2004.
The depth profile in SD-OCT/OFDI can be obtained as the Fourier transform (“FFT”) of the spectral interference in a Michelson interferometer as described in Fercher et al. “Measurement of Intraocular Distances by Backscattering Spectral Interferometry”, Optics Communications, 117(1-2), 43 (1995) and Wojtkowski et al. “In vivo human retinal imaging by Fourier domain optical coherence tomography”, J. Biomed. Opt. 7(3), 457 (2002). The data processing steps to generate a good quality structural SD-OCT image have been described in Cense et al. “Ultrahigh-resolution high-speed retinal imaging using spectral-domain optical coherence tomography”, Opt. Express, 12(11), 2435 (2004), Yun et al. “High-speed optical frequency-domain imaging”, Opt. Express, 11(22), 2953 (2003), and Nassif et al. “In vivo high-resolution video-rate spectral-domain optical coherence tomography of the human retina and optic nerve”, Opt. Express, 12(3), 367 (2004). Various dispersion compensation techniques for OCT have been described in Marks et al. “Autofocus algorithm for dispersion correction in optical coherence tomography”, Appl. Opt., 42(16), 3038 (2003), Marks et al. “Digital algorithm for dispersion correction in optical coherence tomography for homogeneous and stratified media”, Appl. Opt., 42(2), 204 (2003), Wojtkowski et al. “Ultrahigh-resolution, high-speed, Fourier domain optical coherence tomography and methods for dispersion compensation”, Opt. Express, 12(11), 2404 (2004), and Fercher et al. “Numerical dispersion compensation for Partial Coherence Interferometry and Optical Coherence Tomography”, Opt. Express, 9(12), 610 (2001).
In ophthalmic applications, it has been suggested that OCT may be helpful for diagnosing glaucoma by measuring the thickness of the retinal nerve fiber layer (RNFL). In publications, the RNFL thickness has been evaluated with time-domain OCT commercial instruments for only a small number of circular scans, in general three, and not as a full map of the retina. A method to generate a large area thickness map of the RNFL is desirable. See e.g., Bourne et al. “Comparability of retinal nerve fiber layer thickness measurements of optical coherence tomography instruments” Invest. Opthalmol. Visual Sci., 46(4), 1280 (2005), Carpineto et al. “Reliability of nerve fiber layer thickness measurements using optical coherence tomography in normal and glaucomatous eyes” Opthalmology, 110(1), 190 (2003), Aydin et al. “Optical coherence tomography assessment of retinal nerve fiber layer thickness changes after glaucoma surgery”, Opthalmology, 110(8), 1506 (2003), and Guedes et al. “Optical coherence tomography measurement of macular and nerve fiber layer thickness in normal and glaucomatous human eyes”, Opthalmology, 110(1), 177 (2003).
Additional extensions of OCT techniques such as polarization-sensitive OCT (“PS-OCT”) can assist in identifying the properties of the RNFL including the layer's birefringence and boundaries as described in Cense et al. “In vivo birefringence and thickness measurements of the human retinal nerve fiber layer using polarization-sensitive optical coherence tomography”, J. Biomed. Opt., 9(1), 121 (2004), International Application PCT/US05/39374 filed Oct. 31, 2005, International Application PCT/US07/66017 filed Apr. 5, 2007 and International Application PCT/US06/15484 filed Apr. 24, 2006. It is believed that birefringence changes of the RNFL may preclude thickness changes and therefore, birefringence measurement can assist in early diagnosis of glaucoma.
Boundary detection has been studied since the early days of computer vision and image processing, and different approaches have been proposed. Segmentation procedures have also been applied to retinal imaging either for estimating the thickness of various retinal layers, as presented in Ishikawa et al. “Macular segmentation with optical coherence tomography”, Invest. Opthalmol. Visual Sci., 46(6), 2012 (2005) and Fernandez et al. “Automated detection of retinal layer structures on optical coherence tomography images”, Opt. Express, 13(25), 10200 (2005), or for evaluating the thickness of the retina, as presented in Koozekanani et al. “Retinal thickness measurements from optical coherence tomography using a Markov boundary model”, IEEE Trans. Medical Imag., 20(9), 900 (2001). Another segmentation technique based on a deformable spline (snake) algorithm has been described in details in Xu and Prince, “Snakes, shapes, and gradient vector flow” IEEE Trans. Image Process., 7(3), 359 (1998) and Kass et al. “Snakes—Active Contour Models”, Int. J. Comput. Vis., 1(4), 321 (1987). As the snake seeks to minimize its overall energy, its shape will converge on the image gradient contour. However, in general, the snake may not be allowed to travel extensively, and proper initialization may be needed. The snake parameters (elasticity, rigidity, viscosity, and external force weight) can be set to allow the snake to follow the boundary for a large number of retinal topographies. Deformable spline procedures are widely used in medical imaging.
A RNFL thickness map is a quantitative assessment and provides evaluation of large retinal areas as compared to a limited number of circular or radial scans measured with the current commercial instruments. The RNFL thickness maps can potentially be used for a thorough evaluation of the RNFL thickness in longitudinal studies of glaucoma progression. These procedures use large area RNFL thickness maps, which may allow for more accurate correlations of RNFL thinning with visual field defects as opposed to individual circular or radial scans that should be measured at precisely the same retinal location, which is very difficult and that give less information. Therefore, a methodology that allows a determination of RNFL thickness maps based on noise suppression and edge detection may be desirable. Also an intuitive representation of OCT data may be desirable for diagnostic purposes by correlating the quantitative RNFL thickness map with an ultra-high resolution OCT movie, therefore providing a comprehensive picture to clinicians.
Accordingly, there is a need to overcome the deficiencies as described herein above.